Classifier systems are currently in vogue as a way of using genetic algorithms to demonstrate machine learning. However, there are a number of difficulties with the formalization that can influence how knowledge is represented and the rate at which the system can learn. Some of the problems are inherent in classifier systems, and one must learn to cope with them, while others are pitfalls waiti...

In Section 4.5, we introduced several methods that use the RBFN as a classifier. This section introduces another classification method called the learning classifier system (LCS), proposed by John Holland [62]. The learning classifier system is a rule-based, message-passing, machine learning paradigm designed to process environmental stimuli, much like the input-to-output mapping provided by a ...

Two standard schemes for learning in classifier systems have been proposed in the literature: the bucket brigade algorithm (BBA) and the profit sharing plan (PSP). The BBA is a local learning scheme which requires less memory and lower peak computation than the PSP, whereas the PSP is a global learning scheme which typically achieves a clearly better performance than the BBA. This “requirement ...

The inclusion of uncertain class information into multi classifier systems (MCS) is the central theme in this thesis. A multi classifier system relies on multiple base classifiers, each of which is trained on a separate view of the problem at hand. Combining their answers will lead to a more accurate final decision. An example would be emotion recognition, with decisions based on observations o...

[6] Jose Aguilar and Mariela Cerrada. Fuzzy classifier system and genetic programming on system identification problems. In Lee Spector, Erik D. Goodman, Annie Wu, W.B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1245–1251, San ...

This thesis presents a new ML algorithm, HCS, taking inspiration from Learning Classifier Systems, Decision Trees and Statistical Hypothesis Testing, aimed at providing clearly understandable models of medical datasets. Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tole...

While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise...